Frequency Components Of Signal Research Articles

Auto Bias Control Technique for Silicon IQ Modulators Using the Dual-Arms Power Dithers

In high-capacity coherent transmission applications such as 400 Gb/s and beyond, silicon inphase and quadrature modulators (SIQMs) are widely used as a coherent transmitter assembly. The bias points of the SIQM deviate the optimum points because of environmental change. Consequently, auto bias control (ABC) is an essential technique to stabilize the bias points of the SIQM. In this paper, we described a novel method to achieve the bias point high-precision control for SIQMs. First, a pair of the differential power dithers are applied to the dual-arms of the channels I and Q, which can compose a push-pull bias locking mode. By this way, the bias error of the channel P induced by the channels I and Q can be eliminated compared with the conventional single-arm ABC method. Then an orthogonal integration operation is used to extract the dither frequency component of the output signal of the SIQM, and the intersections of the orthogonal-integral curves corresponding to the bias points of the channels I, Q, and P are the optimum bias thermal-power. The simulation and experimental results show that our method exhibits a good performance for locking the optimum thermal-power of the SIQM, and the optical signal-to-noise ratio (OSNR) penalty caused by the proposed method for a 512-Gb/s optical signal transmission with a dual-polarization 16-quadrature amplitude modulation format can be neglected in our experiment.

Auditory-inspired adaptive frequency tracking

One of the best frequency trackers to date is the human (mammalian) auditory system, which has evolved through millions of years of resolving classification problems. It is a versatile, elegant and powerful sound processing unit. It excels in detecting, estimating, and classifying multiple targets simultaneously even in noisy environments. Hence, mimicking even some features of the auditory system could be beneficial in developing superior frequency tracking and classification algorithms. An auditory inspired adaptive synchrony capture filterbank (SCFB) signal processing architecture for tracking signal frequency components was proposed in a related paper [JASA (2013)]. The SCFB architecture consists of a fixed array of traditional, passive linear, gammatone filters in cascade with a bank of three adaptively tunable bandpass filters that form a frequency-discriminator-loop (FDL). The SCFB exhibits many desirable properties for processing speech, music, and other complex sounds. In recent work (Dec 2021), the algorithm was modified using adaptive tuning parameters, and a generalized way to determine/suppress voiced and unvoiced (silent) regions. This modified algorithm estimates frequencies with higher accuracy even in the presence of closely spaced input tones. Preliminary analysis with synthetic, human speech and humpback/whale-call signals demonstrates that the revised algorithm performs well. This talk will focus on the latest updates.

Effects of ionosphere dispersion on wideband GNSS signals

Wideband GNSS signals suffer signal distortions such as waveform deformations and correlation peak reduction when traverse the ionosphere. Basing on the standard model of the ionosphere, we first demonstrate a modified ionosphere model to capture the ionosphere dispersion effects on wideband signals. We decompose the first-order ionosphere model into Taylor series. By using the first three terms of Taylor series, it is possible to account for all frequency components of wideband signals rather than treating them as single tone. We then make an analysis of the ionosphere dispersion effects on wideband GNSS signal tracking. It is revealed that the ionosphere dispersion degrades correlation peak results and shifts carrier-phase in the phase locked loop (PLL) output but dose not cause an additional delay for code measurements. Furthermore, we carry out a simulation for evaluating the ionosphere dispersion effects on tracking of various new generation wideband GNSS signals such as Galileo E5 AltBOC(15, 10) signals and BDS B3 BOC(15, 2.5) signals during ionosphere quietness and activities. The results show that the wider the bandwidth and the greater the total electron content (TEC) values, the more dramatic the ionosphere effects are. The Galileo E5 AltBOC(15, 10) signals are most affected among various wideband GNSS signals. For AltBOC(15, 10) signal tracking the correlation power loss is around 0.1 dB and the carrier-phase change is about 20° caused by the dispersion in quiet ionosphere case, and increases up to 0.35 dB and 33° during ionosphere activities, respectively.

Composite Fault Feature Enhancement Approach for Rolling Bearings Grounded on ITD and Entropy-based Weight Method

Aiming to precisely identify a compound fault of rolling bearing, the paper has contributed a fault characteristic enhancement method by combing entropy weight method (EWM) and intrinsic time scale decomposition (ITD). Firstly, to effectively segregate frequency components in vibration signals, proper rotation components (PRCs) were obtained by decomposing vibration signals based on ITD. Secondly, in view of the fact that amplitude, variance and correlation coefficient vary greatly in a bearing fault accompanied by impact components, parameter evaluation indexes were brought in to depict the fault characteristics of PRCs, including average, variance, correlation coefficient, margin factor, kurtosis, impulse factor, peak factor and so on. Thirdly, weight coefficient of each parameter index was calculated by entropy weight method and the characteristics of each PRC highlighted based on that. Finally, the signals were reconstructed according to the PRCs whose characteristics had been enhanced. Meanwhile reconstructed signals were denoised with singular differential spectrum (SDS) to reduce the influence of noise components, and then the type of compound fault was distinguished grounded on the frequency spectrum. To further prove the efficiency of proposed method, it is compared with other methods (SDS, ITD + entropy method). The result indicates that the proposed method can further highlight the characteristic information of compound faults of bearing and embody more exact identification and judgment on the type of faults.

Experimental investigation on characteristics of cavitation-induced vibration on the runner of a bulb turbine

Cavitation may result in reduced efficiency, increased noise and vibration and weight loss of a hydraulic turbine when local pressure is lower than the vaporization pressure. In this paper, a synchronous test system including high-speed camera and Laser Doppler Vibrometer (LDV) is established to analyze characteristics of cavitation-induced vibration on the runner of a bulb turbine under different cavitation states. The results show that the peak-to-peak and Root Mean Square (RMS) values of vibration velocities on the turbine runner both increase with the severity of cavitation, but the growth rate is remarkably different which strongly depends on the cavitation state. The runner vibration velocity holds a normal distribution for all cavitation states, and the variance of the normal distribution increases gradually with the severity of cavitation. The increase of cavitation level is accompanied by frequency band migration of vibration signals. In the state of complete cavitation, the local energy extreme of the medium-high frequency component of the runner vibration velocity signal migrates to the low frequency region, resulting in an obvious increase in the energy of the low frequency region.

Calculation of Heartbeat Rate and SpO2 Parameters Using a Smartphone Camera: Analysis and Testing.

Mathematical and signal-processing methods were used to obtain reliable measurements of the heartbeat pulse rate and information on oxygen concentration in the blood using short video recordings of the index finger attached to a smartphone built-in camera. Various types of smartphones were used with different operating systems (e.g., iOS, Android) and capabilities. A range of processing algorithms were applied to the red-green-blue (RGB) component signals, including mean intensity calculation, moving average smoothing, and quadratic filtering based on the Savitzky-Golay filter. Two approaches-gradient and local maximum methods-were used to determine the pulse rate, which provided similar results. A fast Fourier transform was applied to the signal to correlate the signal's frequency components with the pulse rate. We resolved the signal into its DC and AC components to calculate the ratio-of-ratios of the AC and DC components of the red and green signals, a method which is often used to estimate the oxygen concentration in blood. A series of measurements were performed on healthy human subjects, producing reliable data that compared favorably to benchmark data obtained by commercial and medically approved oximeters. Furthermore, the effect of the video recording duration on the accuracy of the results was investigated.

The use of an artificial neural network for acoustic selections headphone filters

The article aims to examine the influence of various acoustic materials on the usable properties of headphones. Acoustic materials act as filters that modify the incident acoustic waves by attenuating specific frequency components of the acoustic signals. They can therefore be called acoustic filters because they affect the frequency response (bandwidth) of the headphones in question. Therefore, the choice of acoustic materials in headphones with a specific design can have a significant impact on their psychoacoustic properties. Calibrating your headphones is critical to ensure they don't amplify, attenuate or introduce any coloration to your frequencies. To address this problem, a dedicated test setup was designed and constructed to evaluate the frequency response of selected headphone models using seven different acoustic materials (acoustic filters). The final selection of the acoustic filter was made using an artificial neural network. The article presents the measurement methodology, and the obtained frequency response measurements were analyzed and compared.

Effect of Variation in Sampling Time in Speed Regulation of a Synchronous Motor Drive that is Self-Controlled

This paper presents an analysis in the variation of sampling time of a synchronous motor drive that is self-controlled. The benefits of the synchronous motors are replacing classical induction and direct current (DC) motor drives. It is anticipated that they may be more significant in the imminent future. Because permanent magnet synchronous motors drives are preferred to perform vital tasks in most applications such as in the automotive industry and aerospace, the system reliability is usually an important consideration. Owing to these reasons, the sampling time of the drives have to be taken into consideration regarding how strong the impact of variation in sampling time can affect the drive performance. The drive is fed with three phase power and is utilized in speed regulation. Proportional integral (PI) regulatory approach is studied and is implemented in a PI-based speed regulatory system is presented. Simulation results of the motor current speed, and torque signals from the synchronous machine dynamics of the field flux are given. It was shown that an increase in sampling time results in the elimination of high frequency signal components.

Fast tuning of observer-based circadian phase estimator using biometric data

Circadian rhythms play a vital role in maintaining an individual's well-being, and they have been shown to be the product of the master oscillator in the suprachiasmatic nuclei (SCN) located in the brain. The SCN however, is inaccessible for assessment, so existing standards for circadian phase estimation often focus on the use of indirect measurements as proxies for the circadian state. These methods often suffer from severe delays due to invasive methods of sample collection, making online estimation impossible. In this paper, we propose a linear state observer as an elegant solution for continuous phase estimation. This observer-based filter is used in isolating the frequency components of input biometric signals, which are then taken to be the circadian state. We start the design process by fixing the observer's oscillatory frequency at 24 hours, and then we tune its gains using an evolutionary optimization algorithm to extract the target components from individuals' data. The resulting filter was able to provide phase estimates with an average absolute error within 1.5 hours on all test subjects, given their minute-to-minute actigraphy data collected in ambulatory conditions.

Calibration and frequency estimation in sensors for electrical parameter measurement using regression and metaheuristic based models

AbstractCalibration is the backbone of any sensor and measurement philosophy. The conventional calibration techniques for electrical parameter measurement using nonlinear sensors are affected by the repeating analogue signals and lead to errors in measurement. This research paper investigates different regression‐based mathematical models to calibrate the Hall sensor for measuring RMS and the fundamental frequency. The novelty of this research work lies in the feature‐based input modelling to measure RMS current and frequency with an error of 3.37e‐12% and 7.61e‐9%, respectively. The conventional Fourier transform method is compared with six different bio‐inspired metaheuristic algorithms to estimate the frequency components of the analogue signal received from the measurement setup. Apart from the conventional sine waveforms, this paper investigates sawtooth and square waveforms as the periodic signals for determining the frequency components. The results from the comparison study show that the Whale Optimization Algorithm exhibits 1.25% lesser error whilst predicting the features in measured frequency components. Apart from this, the paper identifies a unique combination of features that effectively measures the instantaneous electrical parameter with RMSE, NRMSE, and value of 158.8, 0.51, and 0.44, respectively. Experimentally it is found that the decision tree and random forest regression models calibrate the Hall sensor with 93% less error than their linear and polynomial counterparts.

A time-frequency denoising method for single-channel event-related EEG.

Electroencephalogram (EEG) acquisition is easily affected by various noises, including those from electrocardiogram (ECG), electrooculogram (EOG), and electromyogram (EMG). Because noise interference can significantly limit the study and analysis of brain signals, there is a significant need for the development of improved methods to remove this interference for more accurate measurement of EEG signals. Based on the non-linear and non-stationary characteristics of brain signals, a strategy was developed to denoise brain signals using a time-frequency denoising algorithm framework of short-time Fourier transform (STFT), bidimensional empirical mode decomposition (BEMD), and non-local means (NLM). Time-frequency analysis can reveal the signal frequency component and its evolution process, allowing the elimination of noise according to the signal and noise distribution. BEMD can be used to decompose the time-frequency signals into sub-time-frequency signals for noise removal at different scales. NLM relies on structural self-similarity to locally smooth an image to remove noise and restore its main geometric structure, making this method appropriate for time-frequency signal denoising. The experimental results show that the proposed method can effectively suppress the high-frequency components of brain signals, resulting in a smoother brain signal waveform after denoising. The correlation coefficient of the reference signal, a superposition average of multiple trial signals, and the original single trial signal was determined, and then correlation coefficients were calculated between the reference signal and single trial signals processed by time-frequency denoising, ensemble empirical mode decomposition (EEMD)-independent component analysis (ICA), EEMD-canonical correlation analysis (CCA), and wavelet threshold denoising methods. The correlation coefficient was highest for the signal processed by the time-frequency denoising method and the reference signal, indicating that the single trial signal after time-frequency denoising was most similar to the waveform of the reference signal and suggesting this is a feasible strategy to effectively reduce noise and more accurately determine signals. The proposed time-frequency denoising method exhibits excellent performance with promising potential for practical application.

Disentangling the Frequency Content in Optoacoustics.

Signals acquired by optoacoustic tomography systems have broadband frequency content that encodes information about structures on different physical scales. Concurrent processing and rendering of such broadband signals may result in images with poor contrast and fidelity due to a bias towards low frequency contributions from larger structures. This problem cannot be addressed by filtering different frequency bands and reconstructing them individually, as this procedure leads to artefacts due to its incompatibility with the entangled frequency content of signals generated by structures of different sizes. Here we introduce frequency-band model-based (fbMB) reconstruction to separate frequency-band-specific optoacoustic image components during image formation, thereby enabling structures of all sizes to be rendered with high fidelity. In order to disentangle the overlapping frequency content of image components, fbMB uses soft priors to achieve an optimal trade-off between localization of the components in frequency bands and their structural integrity. We demonstrate that fbMB produces optoacoustic images with improved contrast and fidelity, which reveal anatomical structures in in vivo images of mice in unprecedented detail. These enhancements further improve the accuracy of spectral unmixing in small vasculature. By offering a precise treatment of the frequency components of optoacoustic signals, fbMB improves the quality, accuracy, and quantification of optoacoustic images and provides a method of choice for optoacoustic reconstructions.

Automatic Epileptic Seizure Detection Using PSO-Based Feature Selection and Multilevel Spectral Analysis for EEG Signals

Automatic epileptic seizure detection technologies for clinical diagnosis mainly rely on electroencephalogram (EEG) recordings, which are immensely useful tools for epileptic location and identification. Currently, traditional seizure detection methods based only on single-view features have great limitations for the typical dynamic and nonlinear EEG signals. An objective of this paper is to investigate the effect of multiview feature selection and multilevel spectral analysis methods on the identification of the EEG signals for seizure detection. Here, multiview features are extracted from time domain, frequency domain, and information theory to collect adequate information of EEG signals. And a feature selection algorithm based on particle swarm optimization (PSO) is proposed for automatic seizure detection. Moreover, due to the different frequency components of the EEG signals, they are divided into four kinds of brain waves for multilevel spectral analysis. The effect of these four rhythm waves on seizure detection is compared. Three well-known classifiers are employed to classify EEG signals concerning seizure or nonseizure events. The result shows that the average accuracy, specificity, and sensitivity of classification with the CHB-MIT database are 98.14%, 98.64%, and 96.79%, respectively. The application of the PSO-based feature selection method for automatic seizure detection improves accuracy by 5.99% with the SVM classifier. Compared with the state-of-the-art methods, the proposed method has superior competence with high performance for automatic seizure detection. It is further shown that the feature selection method is an indispensable step in seizure detection. With PSO-based feature selection and multilevel spectral analysis, the θ wave in the frequency range of 4-7 Hz shows better performance in the identification of EEG signals and is more suitable for the proposed method. The PSO-based feature selection algorithm for automatic seizure detection can be a useful assistant tool for clinical diagnosis.

Temporal Disentanglement of Wireless Signal Carriers Based on Quasi-Light-Storage

High-speed communication systems mandate instantaneous microwave frequency measurement for an efficient utilization of the available spectrum. We propose and experimentally demonstrate a novel, real-time microwave frequency measurement approach based on the time-frequency coherence representation of each signal. The temporal discrimination between the spectral components of the incoming microwave signal is realized through the harnessing of stimulated Brillouin scattering-based quasi light storage (QLS). The QLS is achieved by equidistant sampling of a signal spectrum. Consequently, several copies of that signal will be produced in the time domain and can be discriminated from other spectral components. By tuning the central frequency of the comb, the proposed method is fully reconfigurable and feasible for centimeter, millimeter and even THz signals. Here, we present simultaneous discrimination of single and two frequency components of multi-frequency microwave signals. Since the method can be integrated and supports real-time processing, it might become an integral part for efficient bandwidth usage in future wireless communication systems.

Broadband Off-Grid DOA Estimation Using Block Sparse Bayesian Learning for Nonuniform Noise Variance

In this article, an extended block sparse Bayesian learning (SBL) is applied to a linear system for estimating the direction of arrival (DOA). DOA estimation using conventional SBL deteriorates when inconsistent noise occurs (e.g., some sensors in the array are malfunctioning). The SBL is extended to accommodate the nonuniform noise variance and shows superior robustness in DOA estimation owing to its selective usage of sensors under normal conditions. Furthermore, a block SBL framework is adopted to exploit the commonality of DOAs over broadband signal frequency components and their correlation at the array. The transformation matrix in the linear system is composed of replicas for preset arrival angles as its basis. Practically, the DOAs deviate from the on-grid angles in the matrix, which results in degradation by basis mismatch. An off-grid SBL incorporating an inconsistent noise variance and block sparsity is formulated to estimate the DOAs reliably. The extended SBL is applied to simulated and measured data (SWellEx-96), and its results are compared with those from conventional approaches. It exhibits better performance in terms of resolution and denoising. In particular, when a part of the array is malfunctioning, the extended SBL can still estimate the DOAs clearly, whereas other approaches fail.

Shafting Misalignment Malfunction Quantitative Diagnosis Based on Speed Signal SVD-HT and CSF-PPSO-ESN Method.

Aiming at the quantitative diagnosis of shafting misalignment malfunction, a novel method based on speed signal with singular value decomposition and Hilbert transform (SVD-HT) and cubic spline fitting-Pareto particle swarm optimization-echo state network (CSF-PPSO-ESN) method is proposed. The malfunction diagnosis mechanism based on the speed signal is obtained by constructing the shaft misalignment malfunction model. Then, the SVD-HT and CSF-PPSO-ESN methods are applied to obtain the relationship between the shaft misalignment malfunction and the amplitude of the time and the rotation frequency (fr) component of the speed signal. The parameters of the CSF-PPSO-ESN method are settled according to the shaft misalignment malfunction and the fr component of the speed signal. The accuracy of the proposed method is verified by using the fr component of the speed signal and the trained CSF-PPSO-ESN to obtain the value of the shaft misalignment malfunction. The repeating experimental results show that the diagnosing error of the shaft misalignment malfunction can reach less than ±10 μm. The method presented in this paper provides a novel way to diagnose shaft misalignment malfunction quantitatively.

Analysis of Instantaneous Frequency Components of Speech Signals for Epoch Extraction

The major impulse-like excitation in the speech signal is due to abrupt closure of the vocal folds, which takes place at the glottal closure instant (GCI) or epoch in each cycle. GCIs are used in many areas of speech science and technology, such as in prosody modification, voice source analysis, formant extraction and speech synthesis. It is difficult to observe these discontinuities (corresponding to GCIs) in the speech signal because of the superimposed time-varying response of the vocal tract system. This paper examines the phase part of different frequency components of the speech signal to extract epochs. Three analysis methods to decompose the speech signal into different frequency components are considered. These methods are the short-time Fourier transform (STFT), narrow bandpass filtering (NBPF), and single frequency filtering (SFF). The locations of the discontinuities in the speech signal are obtained from the instantaneous frequency (IF) (i.e., the time derivative of the phase) of each of the frequency components. A method for automatic detection of epochs using the amplitude weighted IF is proposed. Performance of the proposed epoch detection method is compared with four state-of-the-art methods in clean and telephone quality speech. The performance of the proposed method is comparable with the performance of the existing epoch detection methods for clean speech but better for telephone quality speech.

The Measurement and Analysis of Dense Frequency Signals Considering New Energy Integration

In power systems, dense frequency signals (DFSs) comprise several adjacent harmonics or inter-harmonics derived from power electronic devices. Signals that contain two or more significant frequency components within any 15 Hz frequency band are considered DFSs. Existing frequency detection methods cannot detect DFSs with precision in a 10-cycle-period. To solve this issue, an improved dense frequency signal detection (DFSD) method is proposed. Firstly, this paper presents a dense frequency model by which a signal can be decomposed into a series of intrinsic mode functions via complete ensemble empirical mode decomposition (CEEMD). Then, singular value decomposition (SVD) is performed to reduce the dimensions and eliminate noise. Finally, the intensive frequency model is solved via an independent component analysis (ICA), and the parameters of each frequency component are calculated. Simulation and experimental results demonstrate that the proposed method can accurately calculate dense frequency parameters. Considering asynchronous sampling, the proposed method has higher accuracy for harmonic and inter-harmonic detection under the IEC framework.

Series Arc Fault Detection Sensor Based on an ABS Rogowski Coil in Medium Voltage

Three types of Rogowski coils were designed and constructed by utilising 3D printing technology: RC1, RC2, and RC3, with the sensors having the same geometrical dimensions but varying number of turns: 20, 50, and 100 respectively. To verify the feasibility and effectiveness of the proposed sensors, a series arc fault generator was set up and high voltage of up to 3-kV was injected. The analysis of the Fast Fourier Transform (FFT) was done by using MATLAB to determine frequency and voltage amplitude. From the analysis, it was determined that the higher the number of turns, the greater the sensitivity. The resonance frequency (MHz) dropped significantly as the number of turns increased. Furthermore, sensor features such as frequency bandwidth and sensitivity are affected by the number of turns, which was thoroughly investigated in this study. The Rogowski coil was created to detect the high frequency component of the series arc fault signal. All the designed sensors reliably detect the series arc fault signal, according to the experimental results.

Characterization, Measurement and Specification of Device Imperfections in Optical Coherent Transceivers

Transceiver imperfections become the primary source of impairment as baud rate and modulation order grow in advanced optical coherent communications. Thus, transceiver imperfections, both linear and nonlinear, need to be appropriately characterized, measured, and specified. Treatments for linear imperfections are relatively mature. This study reviews the transceiver's linear imperfection modeling, characterization, and measurement technologies. In practical applications, in-field measurement using the transceiver and a few low speed additional devices is preferred. In the case of nonlinear imperfections, the situation is complex. One important task is to estimate the nonlinear system performance from the nonlinear characteristics of the devices. In this study, we attempt to establish a connection between them by examining different technologies. Although the orthogonal component has a good correlation with nonlinear system performance, its measurement is prohibitively complex. In the nonlinear noise to power ratio measurement, a certain frequency component of the input signal is notched, and the re-growth component at the notch frequency is measured at the nonlinear device output. The ratio between the re-growth component power and the output signal power is the noise to power ratio. While this method is easy to carry out, it does not correctly estimate nonlinear impairment in general. The reason for this is that the signal incurs different nonlinear responses in two conditions, i.e., with or without a notch. This method accurately estimates the nonlinear impairment in some special but useful cases, such as Gaussian input signals and nonlinear systems whose dominant nonlinear term is the even order term.